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| from tensorflow.keras.models import load_model | |
| # importing the preprocessing steps for the model architecture which i used for transfer learning | |
| from tensorflow.keras.applications.xception import preprocess_input | |
| import numpy as np | |
| from tensorflow.keras.preprocessing.image import load_img, img_to_array | |
| import streamlit as st | |
| import cv2 | |
| # import tensorflow as tf | |
| # print(tf.__version__) | |
| # print(np.__version__) | |
| # print(st.__version__) | |
| # print(cv2.__version__) | |
| st.write('# Cat and Dog Classifier') | |
| st.markdown( | |
| ''' | |
| This app uses transfer learning on the Xception model to predict images of cats and dogs. | |
| It achieved an accuracy of approx. 99 percent on the validation set. | |
| *View on [Github](https://github.com/eskayML/cat-and-dogs-classification)* | |
| > ### Enter an image of either a cat or a dog for the model to predict. | |
| ''' | |
| ) | |
| # image_path = 'sample_images/hang-niu-Tn8DLxwuDMA-unsplash.jpg' | |
| model = load_model('cat_and_dog_classifier.h5') | |
| def test_image(object_image): | |
| # Convert the file to an opencv image. | |
| file_bytes = np.asarray(bytearray(object_image.read()), dtype=np.uint8) | |
| opencv_image = cv2.imdecode(file_bytes, 1) | |
| opencv_image = cv2.resize(opencv_image, (200, 200)) | |
| opencv_image.shape = (1, 200, 200, 3) | |
| opencv_image = preprocess_input(opencv_image) | |
| predictions = model.predict(opencv_image) | |
| if predictions[0, 0] >= 0.5: | |
| result = 'DOG' | |
| confidence = predictions[0, 0] * 100 | |
| else: | |
| result = 'CAT' | |
| confidence = 100 - (predictions[0, 0] * 100) | |
| return result, round(confidence, 2) | |
| # it returns the predicted label and the precision i.e the confidence score | |
| object_image = st.file_uploader("Upload an image...", type=[ | |
| 'png', 'jpg', 'webp', 'jpeg']) | |
| submit = st.button('Predict') | |
| if submit: | |
| if object_image is not None: | |
| output = test_image(object_image) | |
| # Displaying the image | |
| st.image(object_image, channels="BGR") | |
| st.markdown(f"""## This is an image of a: {output[0]} """) | |
| st.write(f'# Confidence: ${ output[1]}$ %') | |
| # print(f'The image was predicted as a {test_image(image_path)}') | |